#####################     Data preparation  ############################

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sys
from scipy.io import arff



from scipy.io import arff
!pip install ydata-profiling
!jupyter nbextension enable --py widgetsnbextension
!pip install matplotlib
!pip install graphviz

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import pandas as pd
from scipy.io import arff

data_file = "churn.arff"

# Load ARFF file
data, meta = arff.loadarff(data_file)

# Convert data to DataFrame
df = pd.DataFrame(data)

# Decode object columns if needed
for col in df.columns:
    if df[col].dtype == 'object':
        df[col] = df[col].str.decode('utf-8')

# Look at loaded data and data types
print(df.dtypes)

State                            object
Account Length                  float64
Area Code                        object
Phone Number                     object
Inter Plan                       object
VoiceMail Plan                   object
No of Vmail Mesgs               float64
Total Day Min                   float64
Total Day calls                 float64
Total Day Charge                float64
Total Evening Min               float64
Total Evening Calls             float64
Total Evening Charge            float64
Total Night Minutes             float64
Total Night Calls               float64
Total Night Charge              float64
Total Int Min                   float64
Total Int Calls                 float64
Total Int Charge                float64
No of Calls Customer Service    float64
Churn                            object
dtype: object

# Display the first few rows of the DataFrame
df.head(10)

# look at meta information about data, such as null values
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3333 entries, 0 to 3332
Data columns (total 21 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   State                         3333 non-null   object 
 1   Account Length                3333 non-null   float64
 2   Area Code                     3333 non-null   object 
 3   Phone Number                  3333 non-null   object 
 4   Inter Plan                    3333 non-null   object 
 5   VoiceMail Plan                3333 non-null   object 
 6   No of Vmail Mesgs             3333 non-null   float64
 7   Total Day Min                 3333 non-null   float64
 8   Total Day calls               3333 non-null   float64
 9   Total Day Charge              3333 non-null   float64
 10  Total Evening Min             3333 non-null   float64
 11  Total Evening Calls           3333 non-null   float64
 12  Total Evening Charge          3333 non-null   float64
 13  Total Night Minutes           3333 non-null   float64
 14  Total Night Calls             3333 non-null   float64
 15  Total Night Charge            3333 non-null   float64
 16  Total Int Min                 3333 non-null   float64
 17  Total Int Calls               3333 non-null   float64
 18  Total Int Charge              3333 non-null   float64
 19  No of Calls Customer Service  3333 non-null   float64
 20  Churn                         3333 non-null   object 
dtypes: float64(15), object(6)
memory usage: 546.9+ KB

# Find max, min, mean and standard deviation of attributes.

df.describe()

df.shape

(3333, 21)

column_names = df.columns
column_names

Index(['State', 'Account Length', 'Area Code', 'Phone Number', 'Inter Plan',
       'VoiceMail Plan', 'No of Vmail Mesgs', 'Total Day Min',
       'Total Day calls', 'Total Day Charge', 'Total Evening Min',
       'Total Evening Calls', 'Total Evening Charge', 'Total Night Minutes',
       'Total Night Calls', 'Total Night Charge', 'Total Int Min',
       'Total Int Calls', 'Total Int Charge', 'No of Calls Customer Service',
       'Churn'],
      dtype='object')

# Finding missing values 

df.isnull().sum()

State                           0
Account Length                  0
Area Code                       0
Phone Number                    0
Inter Plan                      0
VoiceMail Plan                  0
No of Vmail Mesgs               0
Total Day Min                   0
Total Day calls                 0
Total Day Charge                0
Total Evening Min               0
Total Evening Calls             0
Total Evening Charge            0
Total Night Minutes             0
Total Night Calls               0
Total Night Charge              0
Total Int Min                   0
Total Int Calls                 0
Total Int Charge                0
No of Calls Customer Service    0
Churn                           0
dtype: int64

# Handle duplicates

print(df.drop_duplicates(inplace=True))

None

# Identify numerical variables

numeric_variables = df.select_dtypes(include=['int64', 'float64']).columns.tolist()

# Print the list of numerical variables

print("Numerical Variables:")
print(numeric_variables)

Numerical Variables:
['Account Length', 'No of Vmail Mesgs', 'Total Day Min', 'Total Day calls', 'Total Day Charge', 'Total Evening Min', 'Total Evening Calls', 'Total Evening Charge', 'Total Night Minutes', 'Total Night Calls', 'Total Night Charge', 'Total Int Min', 'Total Int Calls', 'Total Int Charge', 'No of Calls Customer Service']

# Identify categorical variables

categorical_variables = df.select_dtypes(include=['object']).columns.tolist()

# Print the list of categorical variables

print("Categorical Variables:")
print(categorical_variables)

Categorical Variables:
['State', 'Area Code', 'Phone Number', 'Inter Plan', 'VoiceMail Plan', 'Churn']

#Determine any outlier values(records)for numeric attributes and create box plots 



# Select numeric attributes
numeric_attributes = df.select_dtypes(include=['int64', 'float64']).columns

# Calculate the number of rows needed for the subplots

num_attributes = len(numeric_attributes)
num_rows = (num_attributes // 3) + (num_attributes % 3 > 0)

# Create box plots for numeric attributes

plt.figure(figsize=(16, 4 * num_rows))
for i, column in enumerate(numeric_attributes, 1):
    plt.subplot(num_rows, 3, i)
    sns.boxplot(x=df[column])
    plt.title(f'Box plot for {column}')

plt.tight_layout()
plt.show()

# To analyze the distribution of numeric attributes and create Histogram



num_attributes = len(numeric_attributes)


num_cols = 3  # Number of columns in the subplot grid
num_rows = (num_attributes // num_cols) + (num_attributes % num_cols > 0)

plt.figure(figsize=(15, 5 * num_rows))

# Plot histograms for numeric attributes

for i, column in enumerate(numeric_attributes, 1):
    plt.subplot(num_rows, num_cols, i)
    sns.histplot(df[column], kde=True)
    plt.title(f'Histogram for {column}')
    plt.xlabel(column)
    plt.ylabel('Frequency')

plt.tight_layout()
plt.show()

/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/zhila/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

# Select only numerical columns
numeric_df = df.select_dtypes(include=['float64', 'int64'])

# Calculate correlation matrix
correlation_matrix = numeric_df.corr()

# Plot correlation matrix
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix')
plt.show()

####### Construct contingency table and perform chi-squared test to assess association between 'State' and 'Churn' variables



from scipy.stats import chi2_contingency

# Contingency table
contingency_table = pd.crosstab(df['State'], df['Churn'])

# Chi-squared test
chi2, p, _, _ = chi2_contingency(contingency_table)
print(f"Chi-squared p-value: {p}")

Chi-squared p-value: 0.002296221552011188

####  Determine whether the dataset has an imbalanced class distribution #####


# Check the class distribution of the target variable

class_distribution = df['Churn'].value_counts()

# Print the class distribution

print("Class Distribution:")

print(class_distribution)

# Check if the dataset has an imbalanced class distribution

is_imbalanced = class_distribution.nunique() > 1

# Print the result

if is_imbalanced:
    print("The dataset has an imbalanced class distribution.")
else:
    print("The dataset has a balanced class distribution.")

Class Distribution:
Churn
FALSE    2850
TRUE      483
Name: count, dtype: int64
The dataset has an imbalanced class distribution.

#Let's create a list for our categorical columns for  Churn data set 



cat_cols = ["State", "Area Code", "Phone Number", "Inter Plan", "VoiceMail Plan"]


# Create a copy of the data frame in memory with a different name
df_onehot = df.copy()

# Convert only categorical variables/features to dummy/one-hot features
df_onehot = pd.get_dummies(df_onehot, columns=cat_cols, prefix=cat_cols)

# Print the dataset
print(df_onehot)


# Create a copy of the data frame in memory with a different name
df_onehot=df.copy()

#convert only categorical variables/features to dummy/one-hot features
df_onehot = pd.get_dummies(df, columns=cat_cols, prefix = cat_cols)

#print the dataset
df_onehot

      Account Length  No of Vmail Mesgs  Total Day Min  Total Day calls  \
0              107.0               26.0          161.6            123.0   
1              137.0                0.0          243.4            114.0   
2               84.0                0.0          299.4             71.0   
3               75.0                0.0          166.7            113.0   
4              118.0                0.0          223.4             98.0   
...              ...                ...            ...              ...   
3328            68.0                0.0          231.1             57.0   
3329            28.0                0.0          180.8            109.0   
3330           184.0                0.0          213.8            105.0   
3331            74.0               25.0          234.4            113.0   
3332           128.0               25.0          265.1            110.0   

      Total Day Charge  Total Evening Min  Total Evening Calls  \
0                27.47              195.5                103.0   
1                41.38              121.2                110.0   
2                50.90               61.9                 88.0   
3                28.34              148.3                122.0   
4                37.98              220.6                101.0   
...                ...                ...                  ...   
3328             39.29              153.4                 55.0   
3329             30.74              288.8                 58.0   
3330             36.35              159.6                 84.0   
3331             39.85              265.9                 82.0   
3332             45.07              197.4                 99.0   

      Total Evening Charge  Total Night Minutes  Total Night Calls  ...  \
0                    16.62                254.4              103.0  ...   
1                    10.30                162.6              104.0  ...   
2                     5.26                196.9               89.0  ...   
3                    12.61                186.9              121.0  ...   
4                    18.75                203.9              118.0  ...   
...                    ...                  ...                ...  ...   
3328                 13.04                191.3              123.0  ...   
3329                 24.55                191.9               91.0  ...   
3330                 13.57                139.2              137.0  ...   
3331                 22.60                241.4               77.0  ...   
3332                 16.78                244.7               91.0  ...   

      Phone Number_422-6690  Phone Number_422-7728  Phone Number_422-8268  \
0                     False                  False                  False   
1                     False                  False                  False   
2                     False                  False                  False   
3                     False                  False                  False   
4                     False                  False                  False   
...                     ...                    ...                    ...   
3328                  False                  False                  False   
3329                  False                  False                  False   
3330                  False                  False                  False   
3331                  False                  False                  False   
3332                  False                  False                  False   

      Phone Number_422-8333  Phone Number_422-8344 Phone Number_422-9964  \
0                     False                  False                 False   
1                     False                  False                 False   
2                     False                  False                 False   
3                     False                  False                 False   
4                     False                  False                 False   
...                     ...                    ...                   ...   
3328                  False                  False                 False   
3329                  False                  False                 False   
3330                  False                  False                 False   
3331                  False                  False                 False   
3332                  False                  False                 False   

      Inter Plan_no  Inter Plan_yes  VoiceMail Plan_no  VoiceMail Plan_yes  
0              True           False              False                True  
1              True           False               True               False  
2             False            True               True               False  
3             False            True               True               False  
4             False            True               True               False  
...             ...             ...                ...                 ...  
3328           True           False               True               False  
3329           True           False               True               False  
3330          False            True               True               False  
3331           True           False              False                True  
3332           True           False              False                True  

[3333 rows x 3407 columns]

#Repeat the train test set split

from sklearn.model_selection import train_test_split

class_col_name="Churn"
one_hot_feature_names=df_onehot.columns[df_onehot.columns != class_col_name]
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(df_onehot.loc[:, one_hot_feature_names], df_onehot[class_col_name], test_size=0.3,random_state=109) # 70% training and 30% test

# Repeat Naive Bayes modeling
from sklearn.naive_bayes import MultinomialNB

#Create a MultiNomial NB Classifier
nb = MultinomialNB()

#Train the model using the training sets
nb.fit(X_train, y_train)

#Predict the response for test dataset
y_pred = nb.predict(X_test)
print ("Succesfully done..")

Succesfully done..

print("Number of features used ",nb.n_features_in_)
print("Classes ",nb.classes_)
print("Number of records for classes ",nb.class_count_)
print("Log prior probability for classes ", nb.class_log_prior_)
print("Log conditional probability for each feature given a class\n",nb.feature_log_prob_)

Number of features used  3406
Classes  ['FALSE' 'TRUE']
Number of records for classes  [2000.  333.]
Log prior probability for classes  [-0.15400781 -1.94676778]
Log conditional probability for each feature given a class
 [[-2.35240444 -4.81236658 -1.8039874  ... -9.66722848 -7.32471068
  -8.18058615]
 [-2.38746885 -5.36209714 -1.70638185 ... -8.24401576 -7.2151181
  -8.7786926 ]]

from sklearn.metrics import confusion_matrix
cf=confusion_matrix(y_test, y_pred)
print ("Confusion Matrix")
print(cf)
tn, fp, fn, tp=cf.ravel()
print ("TP: ", tp,", FP: ", fp,", TN: ", tn,", FN:", fn)

Confusion Matrix
[[758  92]
 [ 84  66]]
TP:  66 , FP:  92 , TN:  758 , FN: 84

from sklearn.metrics import classification_report
from sklearn import metrics

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

       FALSE       0.90      0.89      0.90       850
        TRUE       0.42      0.44      0.43       150

    accuracy                           0.82      1000
   macro avg       0.66      0.67      0.66      1000
weighted avg       0.83      0.82      0.83      1000

from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=5)
clf = clf.fit(X_train, y_train)
import graphviz
#Get unique class values to display on the tree
class_values=df_onehot[class_col_name].unique()
print ("class Names",class_values)

dot_data = tree.export_graphviz(clf, out_file=None,
                                feature_names=one_hot_feature_names,
                                class_names=class_values,
                                filled=True)

# Draw graph
graph = graphviz.Source(dot_data, format="png")
graph

class Names ['FALSE' 'TRUE']

# Perform prediction on the test set
y_pred = clf.predict(X_test)

# Get classification report
from sklearn.metrics import classification_report
from sklearn import metrics

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

       FALSE       0.90      0.89      0.90       850
        TRUE       0.42      0.44      0.43       150

    accuracy                           0.82      1000
   macro avg       0.66      0.67      0.66      1000
weighted avg       0.83      0.82      0.83      1000

from ydata_profiling import ProfileReport

# Generate the data profiling report
report = ProfileReport(df)
report

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	Account Length	No of Vmail Mesgs	Total Day Min	Total Day calls	Total Day Charge	Total Evening Min	Total Evening Calls	Total Evening Charge	Total Night Minutes	Total Night Calls	Total Night Charge	Total Int Min	Total Int Calls	Total Int Charge	No of Calls Customer Service
count	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000	3333.000000
mean	101.064806	8.099010	179.775098	100.435644	30.562307	200.980348	100.114311	17.083540	200.872037	100.107711	9.039325	10.237294	4.479448	2.764581	1.562856
std	39.822106	13.688365	54.467389	20.069084	9.259435	50.713844	19.922625	4.310668	50.573847	19.568609	2.275873	2.791840	2.461214	0.753773	1.315491
min	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	23.200000	33.000000	1.040000	0.000000	0.000000	0.000000	0.000000
25%	74.000000	0.000000	143.700000	87.000000	24.430000	166.600000	87.000000	14.160000	167.000000	87.000000	7.520000	8.500000	3.000000	2.300000	1.000000
50%	101.000000	0.000000	179.400000	101.000000	30.500000	201.400000	100.000000	17.120000	201.200000	100.000000	9.050000	10.300000	4.000000	2.780000	1.000000
75%	127.000000	20.000000	216.400000	114.000000	36.790000	235.300000	114.000000	20.000000	235.300000	113.000000	10.590000	12.100000	6.000000	3.270000	2.000000
max	243.000000	51.000000	350.800000	165.000000	59.640000	363.700000	170.000000	30.910000	395.000000	175.000000	17.770000	20.000000	20.000000	5.400000	9.000000

	State	Account Length	Area Code	Phone Number	Inter Plan	VoiceMail Plan	No of Vmail Mesgs	Total Day Min	Total Day calls	Total Day Charge	...	Total Evening Calls	Total Evening Charge	Total Night Minutes	Total Night Calls	Total Night Charge	Total Int Min	Total Int Calls	Total Int Charge	No of Calls Customer Service	Churn
0	OH	107.0	A415	371-7191	no	yes	26.0	161.6	123.0	27.47	...	103.0	16.62	254.4	103.0	11.45	13.7	3.0	3.70	1.0	FALSE
1	NJ	137.0	A415	358-1921	no	no	0.0	243.4	114.0	41.38	...	110.0	10.30	162.6	104.0	7.32	12.2	5.0	3.29	0.0	FALSE
2	OH	84.0	A408	375-9999	yes	no	0.0	299.4	71.0	50.90	...	88.0	5.26	196.9	89.0	8.86	6.6	7.0	1.78	2.0	FALSE
3	OK	75.0	A415	330-6626	yes	no	0.0	166.7	113.0	28.34	...	122.0	12.61	186.9	121.0	8.41	10.1	3.0	2.73	3.0	FALSE
4	AL	118.0	A510	391-8027	yes	no	0.0	223.4	98.0	37.98	...	101.0	18.75	203.9	118.0	9.18	6.3	6.0	1.70	0.0	FALSE
5	MA	121.0	A510	355-9993	no	yes	24.0	218.2	88.0	37.09	...	108.0	29.62	212.6	118.0	9.57	7.5	7.0	2.03	3.0	FALSE
6	MO	147.0	A415	329-9001	yes	no	0.0	157.0	79.0	26.69	...	94.0	8.76	211.8	96.0	9.53	7.1	6.0	1.92	0.0	FALSE
7	LA	117.0	A408	335-4719	no	no	0.0	184.5	97.0	31.37	...	80.0	29.89	215.8	90.0	9.71	8.7	4.0	2.35	1.0	FALSE
8	WV	141.0	A415	330-8173	yes	yes	37.0	258.6	84.0	43.96	...	111.0	18.87	326.4	97.0	14.69	11.2	5.0	3.02	0.0	FALSE
9	IN	65.0	A415	329-6603	no	no	0.0	129.1	137.0	21.95	...	83.0	19.42	208.8	111.0	9.40	12.7	6.0	3.43	4.0	TRUE